Evidence map›Paper›PMID 38788073›Full record

ArticleJournal of Alzheimer's disease : JAD2024

Prevalence of Mild Cognitive Impairment and Alzheimer's Disease Identified in Veterans in the United States.

Byron J Aguilar, Guneet K Jasuja, Xuyang Li, Ekaterina Shishova, Natalia Palacios, Dan Berlowitz, Peter Morin, Maureen K O'Connor, Andrew Nguyen, Joel Reisman and 5 more

Abstract read
In one paragraph

Article in Journal of Alzheimer's disease : JAD, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Byron J AguilarGeriatric Research Education and Clinical Center, VA Bedford Healthcare System, Bedford, MA, USA.
Guneet K JasujaCenter for Healthcare Organization & Implementation Research, VA Bedford Healthcare System, Bedford, MA, USA.
Xuyang LiThe Bedford VA Research Corporation, Inc., Bedford, MA, USA.
Ekaterina ShishovaDepartment of Public Health, Zuckerberg College of Health Sciences, University of Massachusetts Lowell, Lowell, MA, USA.
Natalia PalaciosGeriatric Research Education and Clinical Center, VA Bedford Healthcare System, Bedford, MA, USA.
Dan BerlowitzDepartment of Public Health, Zuckerberg College of Health Sciences, University of Massachusetts Lowell, Lowell, MA, USA.
Peter MorinDepartment of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.
Maureen K O'ConnorGeriatric Research Education and Clinical Center, VA Bedford Healthcare System, Bedford, MA, USA.
Andrew NguyenGeriatric Research Education and Clinical Center, VA Bedford Healthcare System, Bedford, MA, USA.
Joel ReismanDepartment of Biological Science, Kennedy College of Sciences, University of Massachusetts Lowell, Lowell, MA, USA.
Yue LengDepartment of Psychiatry, University of California San Francisco Weill Institute for Neurosciences, San Francisco, CA, USA.
Raymond ZhangAlzheimer's Disease and Brain Health, Eisai Inc., Nutley, NJ, USA.
Amir Abbas Tahami MonfaredAlzheimer's Disease and Brain Health, Eisai Inc., Nutley, NJ, USA.
Quanwu ZhangAlzheimer's Disease and Brain Health, Eisai Inc., Nutley, NJ, USA.
Weiming XiaGeriatric Research Education and Clinical Center, VA Bedford Healthcare System, Bedford, MA, USA.

Funding

Big data and small molecules for Alzheimer's diseaseRF1AG063913 · NIA · BOSTON UNIVERSITY MEDICAL CAMPUS · PI GOLDSTEIN, LEE E., XIA, WEIMING · 2019 to 2021
$2.9M
NIA NIH HHS RF1 AG063913
6 · The paper itself

Abstract

Background: Diagnostic codes can be instrumental for case identification in Alzheimer's disease (AD) research; however, this method has known limitations and cannot distinguish between disease stages. Clinical notes may offer more detailed information including AD severity and can complement diagnostic codes for case identification. Objective: To estimate prevalence of mild cognitive impairment (MCI) and AD using diagnostics codes and clinical notes available in the electronic healthcare record (EHR). Methods: This was a retrospective study in the Veterans Affairs Healthcare System (VAHS). Health records from Veterans aged 65 years or older were reviewed during Fiscal Years (FY) 2010-2019. Overall, 274,736 and 469,569 Veterans were identified based on a rule-based algorithm as having at least one clinical note for MCI and AD, respectively; 201,211 and 149,779 Veterans had a diagnostic code for MCI and AD, respectively. During FY 2011-2018, likely MCI or AD diagnosis was defined by≥2 qualifiers (i.e., notes and/or codes)≥30 days apart. Veterans with only 1 qualifier were considered as suspected MCI/AD. Results: Over the 8-year study, 147,106 and 207,225 Veterans had likely MCI and AD, respectively. From 2011 to 2018, yearly MCI prevalence increased from 0.9% to 2.2%; yearly AD prevalence slightly decreased from 2.4% to 2.1%; mild AD changed from 22.9% to 26.8%, moderate AD changed from 26.5% to 29.1%, and severe AD changed from 24.6% to 30.7. Conclusions: The relative distribution of AD severities was stable over time. Accurate prevalence estimation is critical for healthcare resource allocation and facilitating patients receiving innovative medicines.

Indexed as

Alzheimer DiseaseCognitive DysfunctionVeteransAgedAged, 80 and overElectronic Health RecordsFemaleHumansMalePrevalenceRetrospective StudiesUnited StatesUnited States Department of Veterans AffairsAlzheimer’s disease,electronic health recordmild cognitive impairmentprevalenceveterans affairs

Identifiers

PMID38788073
PMCPMC11191444

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.